1 GASTOS DE PERSONAL 33.298.640 40,81 2 GASTOS EN BIENES CORRIENTES Y SERVICIOS 24.492.440 30,

3.2.1 Design and Data

This study employed a retrospective cross-sectional analysis of data available from the Ontario Mental Health Reporting System (OMHRS) of the Canadian Institute for Health Information (Canadian Institute for Health Information, 2013). The OMHRS includes data from every person admitted to an inpatient mental health bed across 82 units or hospitals in Ontario, Canada. The sample included 150,600 patients admitted between January 1, 2006, and December 31, 2016. Patients with lengths of stay of less than 72 hours were excluded because these patients were not assessed with the complete RAI-MH assessment. Additionally, forensic patients were

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excluded from the dataset due to the system factors that determine access to care for this population.

3.2.2 Assessment Instrument

The OMHRS is based on information from the Resident Assessment Instrument-Mental Health (RAI-MH). The RAI-MH was mandated in October 2005 by the Ontario Ministry of Health and Long-Term Care for use with each person admitted to an inpatient mental health bed (Perlman et al., 2013). All patients are assessed with the RAI-MH after 72 hours of hospital stay, at 90-days (if applicable), and at discharge. The assessment is completed by trained clinical staff based on observation, interviews with the patient, key informants, and other clinical staff (Hirdes et al., 2000). The RAI-MH has strong interrater reliability with an average agreement for all RAI-MH items of 83% and an average weighted kappa across items of 0.70 (Hirdes et al., 2002; Hirdes et al., 2008).

3.2.3 Independent Variables

The RAI-MH includes items that can be grouped into different categories including demographic information (i.e., age, gender, marital status, living arrangements, employment), referral information, service history, mental status, substance use, cognitive performance, behaviours and violence, harm to self, interventions as well as social, financial, and vocational functioning (Hirdes et al., 2000). The assessment also includes psychiatric diagnoses based on the DSM-IV and V provided by the psychiatrist overseeing the care of the person. Items assessing symptoms, behaviours, and functioning tend to include a 3-day observation period. Others, such as substance use, are based on patterns over specified time periods (e.g., within the prior 3 days to 1 year). The RAI-MH also contains a number of validated summary scales, such as the Positive Symptoms Scale which ranges from 0-12, with scores of 3 or more indicating hallucinations and

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delusions (Perlman et al., 2015); the Depression Severity Index which ranges from 0-12, where a score of 3 or more indicate depressive symptoms (Perlman et al., 2013); the Risk of Harm to Others Scale that ranges from 0 to 6 with scores of 3 or more being predictive of inpatient assaults (Neufeld, Perlman, & Hirdes, 2012); the Cognitive Performance Scale that measures the severity of cognitive impairment on a scale from 0 to 6, where a score of 3 or more indicate moderate to severe impairment (Jones et al., 2010); and the Social Withdrawal Scale where scores of 3 or more indicate moderate to severe social withdrawal (Rios & Perlman, 2017). Additionally, health region was examined using the Local Health Integrated Networks (LHINs), the geographical health region where patients received services. There are fourteen LHINs in Ontario that plan, integrate and fund local health care (Office of the Auditor General of Ontario, 2015).

3.2.4 Dependent Variable: Marginalization Indicators

The contextual level data utilized in this study are based on the Ontario Marginalization Index (ON-Marg) (Matheson et al., 2012b). This geographical index is based on 18 different variables that measure multiple dimensions of marginalization using data from the Canadian Census. The index provides a continuous score for four different aspects of marginalization and can be converted into an ordinal scale from 1 (least) to 5 (most) based on the quintile distribution across geographic units (Matheson et al., 2012a). This dataset also contains the population counts per geographic unit based on Census estimates for 2006. In building an operationalization of area level marginalization for this study, the domains of “residential instability” and “material deprivation” were chosen (Refer to Table 2.1 in Chapter 2 for a list of the variables that make up the ON-Marg Index). The inclusion of these two domains allowed for a comprehensive depiction of area-level marginalization that considers social problems relevant to the individual level data available from OMHRS. In addition to the quintile scores, a combined and dichotomized version

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of these measures was created; where scores of 1, 2 or 3 in “residential instability,” or “material deprivation,” represented “low marginalization,” while scores of 4 or 5 in either domain represented “high marginalization.”

3.2.5 Statistical Analyses

The geographic unit used for this study was the Forward Sortation Area (FSA), which is identified by the first 3 digits of the postal code of a person’s residence. The 2011 Canadian Census indicated that there were 526 FSAs in Ontario. The FSA was available for all individuals in the OMHRS data; however, to link to ON-Marg data, FSA scores for the ON-Marg were calculated by taking the weighted average of Dissemination Areas scores within each FSA, as per the ON- Marg User Manual (Matheson et al., 2012b). To examine the geographic distribution of patient characteristics, bivariate relationships between individual characteristics and the FSA quintile scores for material deprivation and residential instability were assessed using frequency and chi- square statistics (significance level p-value <0.0001).

Multivariate logistic regression models were developed to examine factors that are related to residing in areas of high “residential instability,” “material deprivation,” and a combination of these two domains to represent “marginalization.” Variables selected for these models were determined based both on variables reported to have clinical relevance in the scientific literature and statistical significance presented by the bivariate analysis results. The models were built in stages, testing the effect of variables grouped by demographics, diagnoses, symptoms, social support, and so on. Non-significant variables were deleted sequentially from the models until only significant variables remained. The selection of variables was done manually, omitting one variable each time and reviewing how the coefficients and their relevant standard errors changed, rather than using automated methods, to avoid potential problems with multicollinearity (Graham,

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2003). Similarly, different combinations of the remaining independent variables were examined to rule out collinearity and deletion effects (Hosmer Jr, Lemeshow, & Sturdivant, 2013). P-value of less than 0.001 were considered statistically significant, and odds ratios with 95% confidence intervals were used to assess effect sizes of each variable. The c-statistics (area under the ROC curve) of the models was used to interpret the strength of the models, with a value of 0.70 or higher indicating good model discrimination between those residing in areas of high marginalization versus those living in areas of low marginalization (DeSalvo, Fan, McDonell, & Fihn, 2005). Regarding the categorical variables that were tested in the models, the reference group of “18 years old or less” was selected for the variable measuring age groups and “grade 8 or less” for the variables assessing levels of education. To assess the number of marginalized areas per health region, the Toronto Central LHIN was chosen as the reference group as it was the region with the highest density of resources and services. All analyses were conducted using SAS software version 9.4 using PROC FREQ and PROC LOGISTIC statements.